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Removed landmark detection from ONNX model due to potential compatibility issues.

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Emiliana 2020-08-11 10:44:07 +02:00
parent 2fc0f52385
commit c9eb3c1826
5 changed files with 26 additions and 6 deletions
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tracker.py
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@ -111,6 +111,10 @@ def logit(p, factor=16.0):
p = p/(1-p)
return float(np.log(p)) / float(factor)
def logit_arr(p, factor=16.0):
p = np.clip(p, 0.0000001, 0.9999999)
return np.log(p / (1 - p)) / float(factor)
def matrix_to_quaternion(m):
t = 0.0
q = [0.0, 0.0, 0, 0.0]
@ -132,9 +136,8 @@ def matrix_to_quaternion(m):
return q
def worker_thread(session, frame, input, crop_info, queue, input_name, idx, tracker):
output = session.run([], {input_name: input})
lms = np.array(output[1]) * np.array([crop_info[3], crop_info[2], 1.]) + np.array([crop_info[1], crop_info[0], 0.])
conf = output[0]
output = session.run([], {input_name: input})[0]
conf, lms = tracker.landmarks(output[0], crop_info)
if conf > tracker.threshold:
try:
eye_state = tracker.get_eye_state(frame, lms, single=True)
@ -664,6 +667,24 @@ class Tracker():
results[:, [1,3]] *= frame.shape[0] / 224.
return results
def landmarks(self, tensor, crop_info):
crop_x1, crop_y1, scale_x, scale_y, _ = crop_info
avg_conf = 0
res = self.res - 1
t_main = tensor[0:66].reshape((66,self.out_res_i * self.out_res_i))
t_m = t_main.argmax(1)
indices = np.expand_dims(t_m, 1)
t_conf = np.take_along_axis(t_main, indices, 1).reshape((66,))
t_off_x = np.take_along_axis(tensor[66:132].reshape((66,self.out_res_i * self.out_res_i)), indices, 1).reshape((66,))
t_off_y = np.take_along_axis(tensor[132:198].reshape((66,self.out_res_i * self.out_res_i)), indices, 1).reshape((66,))
t_off_x = np.floor(res * logit_arr(t_off_x, self.logit_factor) + 0.5)
t_off_y = np.floor(res * logit_arr(t_off_y, self.logit_factor) + 0.5)
t_x = crop_y1 + scale_y * (res * np.floor(t_m / self.out_res_i) / self.out_res + t_off_x)
t_y = crop_x1 + scale_x * (res * np.floor(np.mod(t_m, self.out_res_i)) / self.out_res + t_off_y)
avg_conf = np.average(t_conf)
lms = np.stack([t_x, t_y, t_conf], 1)
return (avg_conf, np.array(lms))
def estimate_depth(self, face_info):
lms = np.concatenate((face_info.lms, np.array([[face_info.eye_state[0][1], face_info.eye_state[0][2], face_info.eye_state[0][3]], [face_info.eye_state[1][1], face_info.eye_state[1][2], face_info.eye_state[1][3]]], np.float)), 0)
@ -991,9 +1012,8 @@ class Tracker():
start_model = time.perf_counter()
outputs = {}
if num_crops == 1:
output = self.session.run([], {self.input_name: crops[0]})
lms = np.array(output[1]) * np.array([crop_info[0][3], crop_info[0][2], 1.]) + np.array([crop_info[0][1], crop_info[0][0], 0.])
conf = output[0]
output = self.session.run([], {self.input_name: crops[0]})[0]
conf, lms = self.landmarks(output[0], crop_info[0])
if conf > self.threshold:
try:
eye_state = self.get_eye_state(frame, lms)